Estimating Sparse Precision Matrices from Data with Missing Values

Abstract

We study a simple two step procedure for estimating sparse precision matrices from data with missing values, which is tractable in high-dimensions and does not require imputation of the missing values. We provide rates of convergence for this estimator in the spectral norm, Frobenius norm and element-wise `∞ norm. Simulation studies show that this estimator… (More)

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